146 citations · 183 across the 5 of their papers we have counts for
Showing 2018 · stat.APShow all
3 papers · 2 filters
stat.AP2018★ 7 cited
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments
Timothy NeCamp, Josh Gardner, Christopher Brooks
Randomized experiments ensure robust causal inference that are critical to effective learning analytics research and practice. However, traditional randomized experiments, like A/B…
stat.AP2018★ 2 cited
Dropout Model Evaluation in MOOCs
Josh Gardner, Christopher Brooks
The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we presen…
stat.AP2018
Evaluating Predictive Models of Student Success: Closing the Methodological Gap
Josh Gardner, Christopher Brooks
Model evaluation -- the process of making inferences about the performance of predictive models -- is a critical component of predictive modeling research in learning analytics. We…